import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf

class NN(object):
    def __init__(self):
        self.df = pd.read_csv('NN/scenic_data.csv')

    def create_dataset(self, data, n_steps):
        """构造数据
        """
        x, y = [], []
        for i in range(len(data) - n_steps):
            x.append(data[i:i+n_steps])
            y.append(data[i+n_steps, :18])
        return np.array(x), np.array(y)

    def get_model(self):
        n_steps = 7  # 长度7天
        data = self.df.values
        X, y = self.create_dataset(data, n_steps)

        # 训练集与测试集划分
        train_size = int(len(X) * 0.8)
        X_train, X_test = X[:train_size], X[train_size:]
        y_train, y_test = y[:train_size], y[train_size:]
        
        model =tf.keras.models.Sequential()
        model.add(tf.keras.layers.LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps,X_train.shape[2])))
        model.add(tf.keras.layers.LSTM(50, activation='relu'))
        model.add(tf.keras.layers.Dense(18))
        model.compile(optimizer='adam', loss='mse')
        model.fit(X_train,y_train, epochs=50,validation_data=(X_test, y_test))
# 评估
        loss = model.evaluate(X_test, y_test)
        print (f"测试集损失:{loss:}")
            
        tf.kreas.models.save_model(model,'NN/my_model.keras')

if __name__=='__main__':    
    nn= NN()
    nn.get_model()